Supplementary Materials for EBIA-CT: CAM interventions
Gosling CJ and EBIA-CT team
2024-06-23
library(tidyverse)
library(metaumbrella)
library(kableExtra)
collapsunique <- function(x) paste(unique(sort(x)), collapse = ", ")
chemin = "D:/drive_gmail/Recherche/Article 2 - Base de Donnees/7 - Data analysis/data/"
source("4 - ur_analysis140424.R")
res_m = readxl::read_excel(
paste0(chemin, "UR_TOTAL_analysis.xlsx")) %>%
filter(intervention_type == "Complementary")S1. PRIOR checklist
| overall_section | precise_section | item_number | item | location_reported |
|---|---|---|---|---|
| Title | Title | 1 | Identify the report as an overview of reviews | we identified it as large-scale synthesis in the title as the term overview/umbrella review is not know by all readers, and more clearly as umbrella review in abstract |
| Abstract | Abstract | 2 | Provide a comprehensive and accurate summary of the purpose, methods, and results of the overview of reviews. | abstract |
| Introduction | Rationale | 3 | Describe the rationale for conducting the overview of reviews in the context of existing knowledge. | first paragraphs of the introduction |
| Introduction | Objectives | 4 | Provide an explicit statement of the objective(s) or question(s) addressed by the overview of reviews. | last paragraph of the introduction |
| Methods | Eligibility criteria | 5a | Specify the inclusion and exclusion criteria for the overview of reviews. If supplemental primary studies were included, this should be stated, with a rationale. | paragraph entitled ‘Search strategy and eligibility criteria’ |
| Methods | Eligibility criteria | 5b | Specify the definition of ‘systematic review’ as used in the inclusion criteria for the overview of reviews. | paragraph entitled ‘Search strategy and eligibility criteria’ |
| Methods | Information sources | 6 | Specify all databases, registers, websites, organizations, reference lists, and other sources searched or consulted to identify systematic reviews and supplemental primary studies (if included). Specify the date when each source was last searched or consulted. | paragraph entitled ‘Search strategy and eligibility criteria’ |
| Methods | Search strategy | 7 | Present the full search strategies for all databases, registers and websites, such that they could be reproduced. Describe any search filters and limits applied. | paragraph entitled ‘Search strategy and eligibility criteria’ |
| Methods | Selection process | 8a | Describe the methods used to decide whether a systematic review or supplemental primary study (if included) met the inclusion criteria of the overview of reviews | paragraph entitled ‘Search strategy and eligibility criteria’ |
| Methods | Selection process | 8b | Describe how overlap in the populations, interventions, comparators, and/or outcomes of systematic reviews was identified and managed during study selection. | paragraph entitled ‘Overlapping meta-analyses’ |
| Methods | Data collection process | 9a | Describe the methods used to collect data from reports | paragraph entitled ‘Data extraction and checking’ |
| Methods | Data collection process | 9b | If applicable, describe the methods used to identify and manage primary study overlap at the level of the comparison and outcome during data collection. For each outcome, specify the method used to illustrate and/or quantify the degree of primary study overlap across systematic reviews. | not applicable, we selected 1 SR/MA per PICO |
| Methods | Data collection process | 9c | If applicable, specify the methods used to manage discrepant data across systematic reviews during data collection. | paragraphs entitled ‘Data extraction and checking’ and ‘Overlapping meta-analyses’ |
| Methods | Data items | 10 | List and define all variables and outcomes for which data were sought. Describe any assumptions made and/or measures taken to identify and clarify missing or unclear information. | paragraph entitled ‘Search strategy and eligibility criteria’ |
| Methods | Risk of bias assessment | 11a | Describe the methods used to assess risk of bias or methodological quality of the included systematic reviews. | paragraph entitled ‘Assessment of the methodological quality’ |
| Methods | Risk of bias assessment | 11b | Describe the methods used to collect data on (from the systematic reviews) and/or assess the risk of bias of the primary studies included in the systematic reviews. Provide a justification for instances where flawed, incomplete, or missing assessments are identified but not re-assessed. | paragraph entitled ‘Assessment of the methodological quality’ |
| Methods | Risk of bias assessment | 11c | Describe the methods used to assess the risk of bias of supplemental primary studies (if included). | not applicable |
| Methods | Synthesis methods | 12a | Describe the methods used to summarize or synthesize results and provide a rationale for the choice(s). | paragraph entitled ‘Data analysis’ |
| Methods | Synthesis methods | 12b | Describe any methods used to explore possible causes of heterogeneity among results. | paragraph entitled ‘Data analysis’ |
| Methods | Synthesis methods | 12c | Describe any sensitivity analyses conducted to assess the robustness of the synthesized results. | paragraph entitled ‘Data analysis’ |
| Methods | Reporting bias assessment | 13 | Describe the methods used to collect data on (from the systematic reviews) and/or assess the risk of bias due to missing results in a summary or synthesis (arising from reporting biases at the levels of the systematic reviews, primary studies, and supplemental primary studies, if included). | not applicable |
| Methods | Certainty assessment | 14 | Describe the methods used to collect data on (from the systematic reviews) and/or assess certainty (or confidence) in the body of evidence for an outcome. | paragraph entitled ‘Assessment of the certainty of evidence’ |
| Results | Systematic review and supplemental primary study selection | 15a | Describe the results of the search and selection process, including the number of records screened, assessed for eligibility, and included in the overview of reviews, ideally with a flow diagram. | paragraph entitled ‘Results of the searches.’ |
| Results | Characteristics of systematic reviews and supplemental primary studies | 15b | Provide a list of studies that might appear to meet the inclusion criteria, but were excluded, with the main reason for exclusion. | paragraph entitled ’’ |
| Results | Characteristics of systematic reviews and supplemental primary studies | 16 | Cite each included systematic review and supplemental primary study (if included) and present its characteristics. | paragraph entitled ’’ |
| Results | Primary study overlap | 17 | Describe the extent of primary study overlap across the included systematic reviews. | paragraph entitled ’’ |
| Results | Risk of bias in systematic reviews, primary studies, and supplemental primary studies | 18a | Present assessments of risk of bias or methodological quality for each included systematic review. | paragraph entitled ’’ |
| Results | Risk of bias in systematic reviews, primary studies, and supplemental primary studies | 18b | Present assessments (collected from systematic reviews or assessed anew) of the risk of bias of the primary studies included in the systematic reviews. | paragraph entitled ’’ |
| Results | Risk of bias in systematic reviews, primary studies, and supplemental primary studies | 18c | Present assessments of the risk of bias of supplemental primary studies (if included). | paragraph entitled ’’ |
| Results | Summary or synthesis of results | 19a | For all outcomes, summarize the evidence from the systematic reviews and supplemental primary studies (if included). If meta-analyses were done, present for each the summary estimate and its precision and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect. | paragraph entitled ’’ |
| Results | Summary or synthesis of results | 19b | If meta-analyses were done, present results of all investigations of possible causes of heterogeneity. | paragraph entitled ’’ |
| Results | Summary or synthesis of results | 19c | If meta-analyses were done, present results of all sensitivity analyses conducted to assess the robustness of synthesized results. | paragraph entitled ’’ |
| Results | Reporting biases | 20 | Present assessments (collected from systematic reviews and/or assessed anew) of the risk of bias due to missing primary studies, analyses, or results in a summary or synthesis (arising from reporting biases at the levels of the systematic reviews, primary studies, and supplemental primary studies, if included) for each summary or synthesis assessed. | paragraph entitled ’’ |
| Results | Certainty of evidence | 21 | Present assessments (collected or assessed anew) of certainty (or confidence) in the body of evidence for each outcome | paragraph entitled ’’ |
| Discussion | Discussion | 22a | Summarize the main findings, including any discrepancies in findings across the included systematic reviews and supplemental primary studies (if included). | paragraph entitled ’’ |
| Discussion | Discussion | 22b | Provide a general interpretation of the results in the context of other evidence. | paragraph entitled ’’ |
| Discussion | Discussion | 22c | Discuss any limitations of the evidence from systematic reviews, their primary studies, and supplemental primary studies (if included) included in the overview of reviews. Discuss any limitations of the overview of reviews methods used. | paragraph entitled ’’ |
| Discussion | Discussion | 22d | Discuss implications for practice, policy, and future research (both systematic reviews and primary research). Consider the relevance of the findings to the end users of the overview of reviews, e.g., healthcare providers, policymakers, patients, among others. | paragraph entitled ’’ |
| Other information | Registration and protocol | 23a | Provide registration information for the overview of reviews, including register name and registration number, or state that the overview of reviews was not registered. | paragraph entitled ’’ |
| Other information | Registration and protocol | 23b | Indicate where the overview of reviews protocol can be accessed, or state that a protocol was not prepared. | paragraph entitled ’’ |
| Other information | Registration and protocol | 23c | Describe and explain any amendments to information provided at registration or in the protocol. Indicate the stage of the overview of reviews at which amendments were made. | paragraph entitled ’’ |
| Other information | Support | 24 | Describe sources of financial or non-financial support for the overview of reviews, and the role of the funders or sponsors in the overview of reviews. | paragraph entitled ’’ |
| Other information | Competing interests | 25 | Declare any competing interests of the overview of reviews’ authors. | paragraph entitled ’’ |
| Other information | Author information | 26a | Provide contact information for the corresponding author. | paragraph entitled ’’ |
| Other information | Author information | 26b | Describe the contributions of individual authors and identify the guarantor of the overview of reviews. | paragraph entitled ’’ |
| Other information | Availability of data and other materials | 26 | Report which of the following are available, where they can be found, and under which conditions they may be accessed: template data collection forms; data collected from included systematic reviews and supplemental primary studies; analytic code; any other materials used in the overview of reviews. | paragraph entitled ’’ |
S2. Deviations
dat_dev <- officer::docx_summary(officer::read_docx("D:/drive_gmail/Recherche/Article 2 - Base de Donnees/3 - Extraction CAM/8 - article/submission/deviations.docx"))
kbl(data.frame(item=dat_dev$text[1:4],
reason=dat_dev$text[5:8])) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), position = "left") %>%
scroll_box(width = "100%", height = "500px")| item | reason |
|---|---|
| Primary studies. | We finally chose not to limit us to RCT, and also include NRCT. This is because in the field of ASD, many interventions cannot be assessed using RCT (e.g., EIBI). Therefore, this deviation allows to be consistent regardless of the intervention type. |
| Effect measure. | While we anticipated to find meta-analyses of SMD, we found that for safety, this is mostly reported as a dichotomous outcome (presence v absence). Therefore, for adverse effects the preferred metric was RR. |
| Selection process. | We finally chose to select the meta-analysis retained in the primary analysis in situations of overlap not based on the total number of studies included, but based on their methodological quality. This choice is aligned with what we did (and registered) for previous projects. |
| Outcome. | We also extracted information regarding sleep while it was not a preregistered outcome. |
S3. Search strategies
search = officer::docx_summary(sample_doc) %>%
select(text) %>%
unlist() %>%
as.character()
search[search != ""]## [1] "Date = 31/12/2023"
## [2] "PUBMED "
## [3] "(\"Autism Spectrum Disorder\"[Mesh] OR autis*[tw] OR asperger*[tw] OR ASD[tw] OR (Pervasive Development* Disorder*[tw])) AND (meta-analysis[pt] OR (meta analy*[tw]) OR (metaanal*[tw]) OR (Cochrane database syst rev[jour])) "
## [4] "EMBASE "
## [5] "('autism'/exp OR 'autis*':ti,ab,kw OR 'asperger*':ti,ab,kw OR 'ASD':ti,ab,kw OR 'pervasive development* disorder*':ti,ab,kw) AND (’meta-analysis’/exp OR ’meta analy*’:ti,ab,tt,kw OR ’metaanaly*’:ti,ab,tt,kw)"
## [6] "CINAHL "
## [7] "(MH (Autistic disorder) OR MH (Asperger syndrome) OR TI (autis*) OR AB (autis*) OR TI (asperger*) OR AB (asperger*) OR TI (ASD) OR AB (ASD) OR TI (pervasive development* disorder*) OR AB (pervasive development* disorder*)) AND (PT (meta analysis) OR MH (meta analysis) OR TI (meta analy*) OR AB (meta analy*)) "
## [8] "PSYCINFO "
## [9] "(DE \"Autism Spectrum Disorders\" OR TI (autis*) OR AB (autis*) OR TI (asperger*) OR AB (Asperger) OR TI (high functioning) OR AB (high functioning) OR TI (ASD) OR AB(ASD) OR TI (Pervasive Development* Disorder*) OR AB (Pervasive Development* Disorder*) OR TI (PDD) OR AB (PDD) OR TI (HFA) OR AB (HFA) OR TI (Kanner) OR AB (Kanner)) AND (DE \"meta analysis\" OR DE \"systematic review\" OR TI (meta analy*) OR AB (meta analy*) OR TI (systematic review*) OR AB (systematic review*))"
## [10] "Web of Science"
## [11] "(TI=(autis*) OR AB=(autis*) OR TI=(asperger*) OR AB=(asperger*) OR TI=(asd) OR AB=(asd) OR TI=(Pervasive Development* Disorder*) OR AB=(Pervasive Development* Disorder*)) AND (TI=(meta analy*) OR AB=(meta analy*) OR TI=(metaanaly*) OR AB=(metaanaly*))"
## [12] "CENTRAL"
## [13] "MeSH descriptor : [Child Development Disorders, Pervasive] explode all trees OR (autis*):ti,ab,kw OR (asperger*):ti,ab,kw OR (high functioning):ti,ab,kw OR (ASD):ti,ab,kw OR (pervasive development* disorder*):ti,ab,kw OR (PDD):ti,ab,kw OR (HFA):ti,ab,kw OR (Kanner):ti,ab,kw "S4. Classification of age groups
Homogeneous Preschool (<6yo): The 85th percentile of the distribution of the age is <6yo.
Homogeneous School-age (6-12yo): The 25th percentile of the distribution of the age is >=6yo and the 75th percentile of the distribution of the age is < 13yo.
Homogeneous Adolescents (13-19yo): The 25th percentile of the distribution of the age is >=13yo and the 75th percentile of the distribution of the age is < 20yo.
Homogeneous Adults (>=20yo): The 15th percentile of the distribution of the age is >=18yo (because 18 is considered as the adult age in many countries).
S5. Data extraction
For each trial included in the meta-analysis, the first author and at least one member of the research team independently extracted data contained in the meta-analytic report. Comparisons of the extractions were made using the metaConvert R package. Extracted data included the characteristics of participants, such as the number of participants, the average age or age range, the mean total IQ or total IQ range, and the percentage of female participants. It also included data on characteristics of interventions (such as the intervention name, dosage, and length) and outcomes (such as the outcome category, the method used to assess the outcome, and the tool name). Finally, we also extracted information on the study design (NRCT vs. RCT), the type of control group (treatment as usual, eclectic, waiting list/delayed, or active control treatment), the risk of bias, and the effect size (effect size metrics, value, 95% confidence interval, standard error, or variance). When information about a clinical trial, including the age of participants, risk of bias, dosage/duration of an intervention, or type of control group, was absent from one meta-analysis but present in one or more other meta-analyses, the missing information was systematically filled in using the data from the meta-analysis with the highest methodological quality for which the information was available.
S6. Data extraction quality checks
A subsequent critical step following the extraction of data was to either complete or verify the information gathered about the clinical trials included in the meta-analyses. In instances where information pertaining to the same clinical trial differed between two meta-analyses (for instance, if one classified a clinical trial as an RCT and the other as an NRCT), or when the age of the participants in a clinical trial was absent from all available meta-analyses, we proceeded to consult the full text of the clinical trial with the intention of completing or correcting the relevant information. Furthermore, this approach was employed for the estimation of effect sizes that were deemed suspect (e.g., a standardized mean difference exceeding 5, a narrow confidence interval associated with a small sample size). When the full-texts of these clinical trials were not accessible (for example, when the link provided in the references was no longer available), the authors of the meta-analyses were contacted in order to obtain the data. When we did not receive an answer from our request, or when an error was spotted in the estimation of the effect sizes, the meta-analysis was excluded from all our analyses. All calculations about effect sizes were performed using the metaConvert R package.
S7. Details on overlapping meta-analyses
When facing with overlapping meta-analyses, i.e., independent meta-analyses that assessed the same PICO (population, intervention, comparator, outcome) combination, we reported the results of one meta-analysis in our primary analysis, and we reported the results of all other meta-analyses in a secondary analysis aiming to explore their concordance. To select the unique meta-analysis per PICO for our primary analysis, we started to identify all recent reviews (published after January 1st, 2018) that included CCTs with homogeneous age groups, and we selected the meta-analysis with the highest methodological quality at 5 key AMSTAR criteria (that regarded the presence of preregistration, a complete search strategy, a duplicate data selection and extraction, and a risk of bias assessment). When no recent meta-analyses with homogeneous age groups were available, we selected meta-analyses published before 2018 with homogeneous age groups, and ultimately, we selected meta-analyses that pooled together trials with heterogeneous age groups.
S8. Dependent effect sizes
In cases where a given paper reported separate meta-analyses
evaluating the impact of an identical intervention on disparate measures
of the same outcome, the effect sizes of the trials included multiple
times in the separate meta-analyses were aggregated into a single,
unified effect size through the application of the standard Borenstein’s
aggregating approach (Borenstein et al. 2009). Then, all these
indenpendent effect sizes that are quantifying the effects of the same
intervention on the same outcome were pooled together.
For
example, if a meta-analytic report conducted two meta-analyses exploring
the effects of an intervention on the total scores of the CARS-2 and the
ADOS-2, we properly aggregated the effect sizes of the trials included
multiple times in these two meta-analyses and we pooled all the
independent effect sizes into a single meta-analysis exploring the
effects of the intervention on ‘Overall ASD symptoms’.
S9. Included studies
res_included = res_search %>%
filter(Inclusion == "Included")
kbl(res_included[,1:2]) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), position = "left") %>%
scroll_box(width = "100%", height = "500px")| Reference | Inclusion |
|---|---|
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| Maw, S. S., & Haga, C. (2018). Effectiveness of cognitive, developmental, and behavioural interventions for Autism Spectrum Disorder in preschool-aged children: A systematic review and meta-analysis. Heliyon, 4(9). | Included |
| Siafis, S., Çiray, O., Wu, H., Schneider-Thoma, J., Bighelli, I., Krause, M., … & Leucht, S. (2022). Pharmacological and dietary-supplement treatments for autism spectrum disorder: a systematic review and network meta-analysis. Molecular autism, 13(1), 1-17. | Included |
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| Bakermans-Kranenburg MJ, van I Jzendoorn MH. Sniffing around oxytocin: review and meta-analyses of trials in healthy and clinical groups with implications for pharmacotherapy. Transl Psychiatry. 2013;3(5):e258. Published 2013 May 21. doi:10.1038/tp.2013.34 | Included |
| Barahona-Correa, J. B., Velosa, A., Chainho, A., Lopes, R., & Oliveira-Maia, A. J. (2018). Repetitive Transcranial Magnetic Stimulation for Treatment of Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. Frontiers in Integrative Neuroscience, 12. doi:10.3389/fnint.2018.00027 | Included |
| Cai, Q., Feng, L., & Yap, K. Z. (2018). Systematic review and meta-analysis of reported adverse events of long-term intranasal oxytocin treatment for autism spectrum disorder. Psychiatry and Clinical Neurosciences, 72(3), 140–151. doi:10.1111/pcn.12627 | Included |
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| Chen T, Wen R, Liu H, Zhong X, Jiang C. Dance intervention for negative symptoms in individuals with autism spectrum disorder: A systematic review and meta-analysis. Complement Ther Clin Pract. 2022;47:101565. doi:10.1016/j.ctcp.2022.101565 | Included |
| Cheng YS, Tseng PT, Chen YW, et al. Supplementation of omega 3 fatty acids may improve hyperactivity, lethargy, and stereotypy in children with autism spectrum disorders: a meta-analysis of randomized controlled trials. Neuropsychiatr Dis Treat. 2017;13:2531-2543. Published 2017 Oct 4. doi:10.2147/NDT.S147305 | Included |
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| Yu, Y., Huang, J., Chen, X., Fu, J., Wang, X., Pu, L., Gu, C., & Cai, C. (2022). Efficacy and Safety of Diet Therapies in Children With Autism Spectrum Disorder: A Systematic Literature Review and Meta-Analysis. Frontiers in neurology, 13, 844117. | Included |
| Hu L, Du X, Jiang Z, Song C, Liu D. Oxytocin treatment for core symptoms in children with autism spectrum disorder: a systematic review and meta-analysis. Eur J Clin Pharmacol. 2023;79(10):1357-1363. doi:10.1007/s00228-023-03545-w | Included |
| Salazar de Pablo, G., Pastor Jordá, C., Vaquerizo-Serrano, J., Moreno, C., Cabras, A., Arango, C., Hernández, P., Veenstra-VanderWeele, J., Simonoff, E., Fusar-Poli, P., Santosh, P., Cortese, S., & Parellada, M. (2023). Systematic Review and Meta-analysis: Efficacy of Pharmacological Interventions for Irritability and Emotional Dysregulation in Autism Spectrum Disorder and Predictors of Response. Journal of the American Academy of Child and Adolescent Psychiatry, 62(2), 151–168. https://doi.org/10.1016/j.jaac.2022.03.033 | Included |
| Sandbank M, Bottema-Beutel K, Crowley LaPoint S, Feldman J I, Barrett D J, Caldwell N et al. Autism intervention meta-analysis of early childhood studies (Project AIM): updated systematic review and secondary analysis BMJ 2023; 383 :e076733 doi:10.1136/bmj-2023-076733 | Included |
| Wang S, Chen D, Yang Y, Zhu L, Xiong X, Chen A. Effectiveness of physical activity interventions for core symptoms of autism spectrum disorder: A systematic review and meta-analysis. Autism Res. 2023;16(9):1811-1824. doi:10.1002/aur.3004 | Included |
| Ojeda, Á., Barahona-Fuentes, G., Villagra Órdenes, F. et al. Effects of Physical Education on Socializing and Communicating Among Children and Preadolescents with Autism Spectrum Disorder: a Systematic Review and Meta-Analysis. Rev J Autism Dev Disord (2023). https://doi.org/10.1007/s40489-023-00410-5 | Included |
| He X, Liu W, Tang F, Chen X, Song G. Effects of Probiotics on Autism Spectrum Disorder in Children: A Systematic Review and Meta-Analysis of Clinical Trials. Nutrients. 2023;15(6):1415. Published 2023 Mar 15. doi:10.3390/nu15061415 | Included |
| Zhang M, Wu Y, Lu Z, et al. Effects of Vitamin D Supplementation on Children with Autism Spectrum Disorder: A Systematic Review and Meta-analysis. Clin Psychopharmacol Neurosci. 2023;21(2):240-251. doi:10.9758/cpn.2023.21.2.240 | Included |
| Madigand J, Rio M, Vandevelde A. Equine assisted services impact on social skills in autism spectrum disorder: A meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2023;125:110765. doi:10.1016/j.pnpbp.2023.110765 | Included |
| Liu A, Gong C, Wang B, Sun J, Jiang Z. Non-invasive brain stimulation for patient with autism: a systematic review and meta-analysis. Front Psychiatry. 2023;14:1147327. Published 2023 Jun 29. doi:10.3389/fpsyt.2023.1147327 | Included |
| Kiani Z, Farkhondeh T, Aramjoo H, et al. Oxytocin Effect in Adult Patients with Autism: An Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials. CNS Neurol Disord Drug Targets. 2023;22(6):906-915. doi:10.2174/1871527321666220517112612 | Included |
| Jia S, Guo C, Li S, Zhou X, Wang X, Wang Q. The effect of physical exercise on disordered social communication in individuals with autism Spectrum disorder: a systematic review and meta-analysis of randomized controlled trials. Front Pediatr. 2023;11:1193648. Published 2023 Jun 30. doi:10.3389/fped.2023.1193648 | Included |
| Rahim F, Toguzbaeva K, Qasim NH, Dzhusupov KO, Zhumagaliuly A, Khozhamkul R. Probiotics, prebiotics, and synbiotics for patients with autism spectrum disorder: a meta-analysis and umbrella review. Front Nutr. 2023;10:1294089. Published 2023 Dec 11. doi:10.3389/fnut.2023.1294089 | Included |
| Iffland M, Livingstone N, Jorgensen M, Hazell P, Gillies D. Pharmacological intervention for irritability, aggression, and self-injury in autism spectrum disorder (ASD). Cochrane Database Syst Rev. 2023;10(10):CD011769. Published 2023 Oct 9. doi:10.1002/14651858.CD011769.pub2 | Included |
| Xiao N, Shinwari K, Kiselev S, Huang X, Li B, Qi J. Effects of Equine-Assisted Activities and Therapies for Individuals with Autism Spectrum Disorder: Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2023 Feb 1;20(3):2630. doi: 10.3390/ijerph20032630. PMID: 36767996; PMCID: PMC9915993. | Included |
S10. Excluded studies
res_excluded = res_search %>%
filter(Inclusion == "Excluded")
kbl(res_excluded) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), position = "left") %>%
scroll_box(width = "100%", height = "500px")| Reference | Inclusion | Reasons_exclusion |
|---|---|---|
| Ameis, S. H., Kassee, C., Corbett-Dick, P., Cole, L., Dadhwal, S., Lai, M.-C., … Correll, C. U. (2018). Systematic review and guide to management of core and psychiatric symptoms in youth with autism. Acta Psychiatrica Scandinavica. doi:10.1111/acps.12918 | Excluded | Not a meta-analysis |
| Aye, S. Z., Ni, H., Sein, H. H., Mon, S. T., Zheng, Q., & Wong, Y. K. Y. (2021). The effectiveness and adverse effects of D-cycloserine compared with placebo on social and communication skills in individuals with autism spectrum disorder. The Cochrane database of systematic reviews, 2(2), CD013457. | Excluded | Not a meta-analysis |
| Burns, C., Lang, R., & Ledbetter Cho, K. (2017). Meta-analysis of single-case experimental design studies involving children with or at risk of autism spectrum disorder suggests intervention is effective during first three years of life. Evidence-Based Communication Assessment and Intervention, 11(3-4), 119–123. doi:10.1080/17489539.2017.1401038 | Excluded | Comment |
| Charman T, Howlin P, Aldred C, et al. Research into early intervention for children with autism and related disorders: methodological and design issues. Report on a workshop funded by the Wellcome Trust, Institute of Child Health, London, UK, November 2001. Autism. 2003;7(2):217-225. doi:10.1177/1362361303007002008 | Excluded | Not a meta-analysis |
| Classen S, Monahan M. Evidence-based review on interventions and determinants of driving performance in teens with attention deficit hyperactivity disorder or autism spectrum disorder. Traffic Inj Prev. 2013;14(2):188-193. doi:10.1080/15389588.2012.700747 | Excluded | Not a meta-analysis |
| Cook, R., & Botting, D. (1997). Use of orthomolecular therapy for those with behavioural problems and mental handicap: A review. Complementary Therapies in Medicine, 5(4), 228–232. doi:10.1016/s0965-2299(97)80035-0 | Excluded | Not a meta-analysis |
| Correll CU, Cortese S, Croatto G, et al. Efficacy and acceptability of pharmacological, psychosocial, and brain stimulation interventions in children and adolescents with mental disorders: an umbrella review. World Psychiatry. 2021;20(2):244-275. doi:10.1002/wps.20881 | Excluded | Not a meta-analysis of controlled studies |
| Correll CU, Cortese S, Croatto G, et al. Efficacy and acceptability of pharmacological, psychosocial, and brain stimulation interventions in children and adolescents with mental disorders: an umbrella review. World Psychiatry. 2021;20(2):244-275. doi:10.1002/wps.20881 | Excluded | Not a meta-analysis of controlled studies |
| Cortese S, Wang F, Angriman M, Masi G, Bruni O. Sleep Disorders in Children and Adolescents with Autism Spectrum Disorder: Diagnosis, Epidemiology, and Management. CNS Drugs. 2020;34(4):415-423. doi:10.1007/s40263-020-00710-y | Excluded | Not a meta-analysis of controlled studies |
| Cuomo BM, Vaz S, Lee EAL, Thompson C, Rogerson JM, Falkmer T. Effectiveness of Sleep-Based Interventions for Children with Autism Spectrum Disorder: A Meta-Synthesis. Pharmacotherapy. 2017;37(5):555-578. doi:10.1002/phar.1920 | Excluded | Not a meta-analysis of controlled studies |
| Dhir S, Teo WP, Chamberlain SR, Tyler K, Yücel M, Segrave RA. The Effects of Combined Physical and Cognitive Training on Inhibitory Control: A Systematic Review and Meta-Analysis. Neurosci Biobehav Rev. 2021;128:735-748. doi:10.1016/j.neubiorev.2021.07.008 | Excluded | Outcome not included in the UR |
| Guo, S., Zhou, K. L., Dong, S., Xue, X. N., Wei, P. D., Yang, J. Y., Fu, G. B., Liu, Z. B., & Cui, X. (2021). Efficacy and safety of massage therapy for autism spectrum disorders: A protocol for systematic review and meta-analysis. Medicine, 100(19), e25874. | Excluded | Not a meta-analysis |
| https://pubmed.ncbi.nlm.nih.gov/34693989/ | Excluded | Not a meta-analysis of controlled studies |
| https://pubmed.ncbi.nlm.nih.gov/34906264/ | Excluded | Not a meta-analysis of controlled studies |
| Jiang X, Song M, Qin W, Xiao J, Xu X, Yuan Q. Nonpharmaceutical therapy for autism spectrum disorder: A protocol for systematic review and network meta-analysis. Medicine (Baltimore). 2022;101(7):e28811. doi:10.1097/MD.0000000000028811 | Excluded | Protocol |
| Kirkendall, N., & Palokas, M. (2017). Behavioral and/or pharmacological interventions for managing sleep disturbances in children with autism spectrum disorder. JBI Database of Systematic Reviews and Implementation Reports, 15(10), 2495–2501. doi:10.11124/jbisrir-2016-003310 | Excluded | Protocol |
| Lasheras2021 | Excluded | Not a meta-analysis |
| Masi, A., Lampit, A., DeMayo, M. M., Glozier, N., Hickie, I. B., & Guastella, A. J. (2017). A comprehensive systematic review and meta-analysis of pharmacological and dietary supplement interventions in paediatric autism: moderators of treatment response and recommendations for future research. Psychological medicine, 47(7), 1323–1334. | Excluded | Mixed intervention types |
| Masi, A., Lampit, A., Glozier, N., Hickie, I. B., & Guastella, A. J. (2015). Predictors of placebo response in pharmacological and dietary supplement treatment trials in pediatric autism spectrum disorder: a meta-analysis. Translational psychiatry, 5(9), e640. https://doi.org/10.1038/tp.2015.143 | Excluded | Not a meta-analysis of controlled studies |
| Pervin M, Ahmed HU, Hagmayer Y. Effectiveness of interventions for children and adolescents with autism spectrum disorder in high-income vs. lower middle-income countries: An overview of systematic reviews and research papers from LMIC. Front Psychiatry. 2022;13:834783. Published 2022 Aug 4. doi:10.3389/fpsyt.2022.834783 | Excluded | Not a meta-analysis of controlled studies |
| Rosson S, de Filippis R, Croatto G, et al. Brain stimulation and other biological non-pharmacological interventions in mental disorders: An umbrella review. Neurosci Biobehav Rev. 2022;139:104743. doi:10.1016/j.neubiorev.2022.104743 | Excluded | Not a meta-analysis of controlled studies |
| Sandbank, M., Bottema-Beutel, K., Crowley, S., Cassidy, M., Dunham, K., Feldman, J. I., Crank, J., Albarran, S. A., Raj, S., Mahbub, P., & Woynaroski, T. G. (2020). Project AIM: Autism intervention meta-analysis for studies of young children. Psychological bulletin, 146(1), 1–29. | Excluded | Updated later |
| Sturmey P. Treatment of psychopathology in people with intellectual and other disabilities. Can J Psychiatry. 2012;57(10):593-600. doi:10.1177/070674371205701003 | Excluded | Not a meta-analysis of controlled studies |
| Xiong, T., Chen, H., Luo, R., & Mu, D. (2016). Hyperbaric oxygen therapy for people with autism spectrum disorder (ASD). Cochrane Database of Systematic Reviews, (10). | Excluded | Not a meta-analysis |
| NA | Excluded | Meta-analysis of SCD |
| NA | Excluded | Not a meta-analysis of controlled studies |
| Abo Almaali HMM, Gelewkhan A, Mahdi ZAA. Analysis of Evidence-Based Autism Symptoms Enhancement by Acupuncture. J Acupunct Meridian Stud. 2017;10(6):375-384. doi:10.1016/j.jams.2017.09.001 | Excluded | Not a meta-analysis |
| Aithal S, Moula Z, Karkou V, Karaminis T, Powell J, Makris S. A Systematic Review of the Contribution of Dance Movement Psychotherapy Towards the Well-Being of Children With Autism Spectrum Disorders. Front Psychol. 2021;12:719673. Published 2021 Oct 8. doi:10.3389/fpsyg.2021.719673 | Excluded | Not a meta-analysis |
| Akhter M, Khan SM, Firdous SN, Tikmani P, Khan A, Rafique H. A narrative review on manifestations of gluten free casein free diet in autism and autism spectrum disorders. J Pak Med Assoc. 2022;72(10):2054-2060. doi:10.47391/JPMA.3971 | Excluded | Not a meta-analysis |
| Applewhite B, Cankaya Z, Heiderscheit A, Himmerich H. A Systematic Review of Scientific Studies on the Effects of Music in People with or at Risk for Autism Spectrum Disorder. Int J Environ Res Public Health. 2022;19(9):5150. Published 2022 Apr 23. doi:10.3390/ijerph19095150 | Excluded | Not a meta-analysis |
| Auvichayapat N, Auvichayapat P. Transcranial Direct Current Stimulation in Treatment of Child Neuropsychiatric Disorders: Ethical Considerations. Front Hum Neurosci. 2022;16:842013. Published 2022 Jul 8. doi:10.3389/fnhum.2022.842013 | Excluded | Not a meta-analysis |
| Azari, H., Morovati, A., Gargari, B.P. et al. An Updated Systematic Review and Meta-Analysis on the Effects of Probiotics, Prebiotics and Synbiotics in Autism Spectrum Disorder. Rev J Autism Dev Disord (2022). https://doi.org/10.1007/s40489-022-00348-0 | Excluded | Pre/post effect sizes within the experimental group |
| Bang M, Lee SH, Cho SH, et al. Herbal Medicine Treatment for Children with Autism Spectrum Disorder: A Systematic Review. Evid Based Complement Alternat Med. 2017;2017:8614680. doi:10.1155/2017/8614680 | Excluded | Not a meta-analysis |
| Bozzatello, Rocca, Mantelli, & Bellino. (2019). Polyunsaturated Fatty Acids: What is Their Role in Treatment of Psychiatric Disorders? International Journal of Molecular Sciences, 20(21), 5257. doi:10.3390/ijms20215257 | Excluded | Not a meta-analysis of controlled studies |
| Cao J, Chai-Zhang TC, Huang Y, Eshel MN, Kong J. Potential scalp stimulation targets for mental disorders: evidence from neuroimaging studies. J Transl Med. 2021;19(1):343. Published 2021 Aug 10. doi:10.1186/s12967-021-02993-1 | Excluded | Not a meta-analysis of controlled studies |
| Cardoso, C., Kingdon, D., & Ellenbogen, M. A. (2014). A meta-analytic review of the impact of intranasal oxytocin administration on cortisol concentrations during laboratory tasks: Moderation by method and mental health. Psychoneuroendocrinology, 49, 161–170. doi:10.1016/j.psyneuen.2014.07.014 | Excluded | Not on ASD |
| Chan, J. S., Deng, K., & Yan, J. H. (2020). The effectiveness of physical activity interventions on communication and social functioning in autistic children and adolescents: A meta-analysis of controlled trials. Autism, 136236132097764. doi:10.1177/1362361320977645 | Excluded | Mixed intervention types |
| Christie2022 | Excluded | Pre/post effect sizes within the experimental group |
| Cugusi, L., & Carta, M. G. (2020). Conventional exercise interventions for adults with intellectual disabilities: A systematic review and meta-analysis. Translational Sports Medicine. doi:10.1002/tsm2.195 | Excluded | Not on ASD |
| Droboniku, M. J., & Mychailyszyn, M. P. (2021). Animal Interaction Affecting Core Deficit Domains Among Children with Autism: A Meta-Analysis. Journal of Autism and Developmental Disorders. doi:10.1007/s10803-021-04891-3 | Excluded | Pre/post effect sizes within the experimental group |
| Feng K, Zhao Y, Yu Q, Deng J, Wu J, Liu L. Can probiotic supplements improve the symptoms of autism spectrum disorder in children?: A protocol for systematic review and meta analysis. Medicine (Baltimore). 2021;100(10):e18621. doi:10.1097/MD.0000000000018621 | Excluded | Protocol |
| Ferreira, J. P., Ghiarone, T., Júnior, C. R. C., Furtado, G. E., Carvalho, H. M., Rodrigues, A. M., & Toscano, C. V. A. (2019). Effects of Physical Exercise on the Stereotyped Behavior of Children with Autism Spectrum Disorders. Medicina (Kaunas, Lithuania), 55(10), 685. | Excluded | Pre/post effect sizes within the experimental group |
| Fifer, S. (2018). Meta-Analysis of the Efficacy of Neurofeedback (Doctoral dissertation, Walden University). | Excluded | Mixed outcomes |
| Geretsegger, M., Elefant, C., Mössler, K. A., & Gold, C. (2014). Music therapy for people with autism spectrum disorder. Cochrane Database of Systematic Reviews. doi:10.1002/14651858.cd004381.pub3 | Excluded | Updated later |
| Gold, C., Wigram, T., & Elefant, C. (2006). Music therapy for autistic spectrum disorder. Cochrane Database of Systematic Reviews. doi:10.1002/14651858.cd004381.pub2 | Excluded | Updated later |
| Guo S, Zhou KL, Dong S, et al. Efficacy and safety of massage therapy for autism spectrum disorders: A protocol for systematic review and meta-analysis. Medicine (Baltimore). 2021;100(19):e25874. doi:10.1097/MD.0000000000025874 | Excluded | Protocol |
| Hayduke, D., & Nye, A. (2019). C-60 The Efficacy of Weighted Blankets for Quantity and Quality of Sleep in Autism Spectrum Disorder: A Meta-Analysis. Archives of Clinical Neuropsychology, 34(6), 1089–1089. doi:10.1093/arclin/acz034.222 | Excluded | Not enough information to replicate |
| Healy, S., Nacario, A., Braithwaite, R. E., & Hopper, C. (2018). The effect of physical activity interventions on youth with autism spectrum disorder: A meta-analysis. Autism Research, 11(6), 818–833. doi:10.1002/aur.1955 | Excluded | Mixed outcomes |
| Hemangi Narayan Narvekar, Harshada Narayan Narvekar, Canine-assisted Therapy in Neurodevelopmental Disorders: A Scoping Review, European Journal of Integrative Medicine, Volume 50, 2022, 102112, ISSN 1876-3820, https://doi.org/10.1016/j.eujim.2022.102112. | Excluded | Not a meta-analysis of controlled studies |
| Howells, K., Sivaratnam, C., May, T., Lindor, E., McGillivray, J., & Rinehart, N. (2019). Efficacy of Group-Based Organised Physical Activity Participation for Social Outcomes in Children with Autism Spectrum Disorder: A Systematic Review and Meta-analysis. Journal of Autism and Developmental Disorders. doi:10.1007/s10803-019-04050-9 | Excluded | Mixed intervention types |
| https://pubmed.ncbi.nlm.nih.gov/16887860/ | Excluded | Not a meta-analysis of controlled studies |
| https://pubmed.ncbi.nlm.nih.gov/18425890/ | Excluded | Retracted |
| https://pubmed.ncbi.nlm.nih.gov/23575742/ | Excluded | Not on ASD |
| https://pubmed.ncbi.nlm.nih.gov/23781271/ | Excluded | Not on ASD |
| https://pubmed.ncbi.nlm.nih.gov/26094200/ | Excluded | Retracted |
| https://pubmed.ncbi.nlm.nih.gov/26709101/ | Excluded | Not a meta-analysis of controlled studies |
| https://pubmed.ncbi.nlm.nih.gov/27678554/ | Excluded | Not on ASD |
| https://pubmed.ncbi.nlm.nih.gov/28438171/ | Excluded | Not on ASD |
| https://pubmed.ncbi.nlm.nih.gov/33124586/ | Excluded | Not a meta-analysis |
| https://pubmed.ncbi.nlm.nih.gov/33124586/ | Excluded | Not a meta-analysis |
| https://pubmed.ncbi.nlm.nih.gov/34953391/ | Excluded | Not a meta-analysis of controlled studies |
| https://pubmed.ncbi.nlm.nih.gov/36041185/ | Excluded | Not a meta-analysis of controlled studies |
| Huang, Y., Huang, X., Ebstein, R. P., & Yu, R. (2021). Intranasal oxytocin in the treatment of autism spectrum disorders: A multilevel meta-analysis. Neuroscience & Biobehavioral Reviews, 122, 18–27. doi:10.1016/j.neubiorev.2020.12.028 | Excluded | Mixed outcomes |
| Huang, Y., Zhang, B., Cao, J., Yu, S., Wilson, G., Park, J., & Kong, J. (2020). Potential Locations for Noninvasive Brain Stimulation in Treating Autism Spectrum Disorders—A Functional Connectivity Study. Frontiers in Psychiatry, 11. doi:10.3389/fpsyt.2020.00388 | Excluded | Not a meta-analysis of controlled studies |
| Huashuang Z, Yang L, Chensheng H, et al. Prevalence of Adverse Effects Associated With Transcranial Magnetic Stimulation for Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. Front Psychiatry. 2022;13:875591. Published 2022 May 23. doi:10.3389/fpsyt.2022.875591 | Excluded | Not a meta-analysis of controlled studies |
| Hwang, B., & Hughes, C. (2000). Journal of Autism and Developmental Disorders, 30(4), 331–343. doi:10.1023/a:1005579317085 | Excluded | Not a meta-analysis |
| James S, Stevenson SW, Silove N, Williams K. Chelation for autism spectrum disorder (ASD). Cochrane Database Syst Rev. 2015;5(5):CD010766. Published 2015 May 11. doi:10.1002/14651858.CD010766.pub2 | Excluded | Not a meta-analysis |
| Johnstone, J. M., Hughes, A., Goldenberg, J. Z., Romijn, A. R., & Rucklidge, J. J. (2020). Multinutrients for the Treatment of Psychiatric Symptoms in Clinical Samples: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients, 12(11), 3394. doi:10.3390/nu12113394 | Excluded | Not on ASD |
| Kandeel M, El-Deeb W. The Application of Natural Camel Milk Products to Treat Autism-Spectrum Disorders: Risk Assessment and Meta-Analysis of Randomized Clinical Trials. Bioinorg Chem Appl. 2022;2022:6422208. Published 2022 May 27. doi:10.1155/2022/6422208 | Excluded | Retracted |
| Koch, S. C., Riege, R. F. F., Tisborn, K., Biondo, J., Martin, L., & Beelmann, A. (2019). Effects of Dance Movement Therapy and Dance on Health-Related Psychological Outcomes. A Meta-Analysis Update. Frontiers in Psychology, 10. doi:10.3389/fpsyg.2019.01806 | Excluded | Not enough information to replicate |
| Lee B, Lee J, Cheon JH, Sung HK, Cho SH, Chang GT. The Efficacy and Safety of Acupuncture for the Treatment of Children with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis [published correction appears in Evid Based Complement Alternat Med. 2023 Feb 4;2023:9840285]. Evid Based Complement Alternat Med. 2018;2018:1057539. Published 2018 Jan 11. doi:10.1155/2018/1057539 | Excluded | Probable errors in calculations |
| Lee, M. S., Choi, T.-Y., Shin, B.-C., & Ernst, E. (2011). Acupuncture for Children with Autism Spectrum Disorders: A Systematic Review of Randomized Clinical Trials. Journal of Autism and Developmental Disorders, 42(8), 1671–1683. doi:10.1007/s10803-011-1409-4 | Excluded | Not a meta-analysis |
| Lefevre A, Hurlemann R, Grinevich V. Imaging neuropeptide effects on human brain function. Cell Tissue Res. 2019;375(1):279-286. doi:10.1007/s00441-018-2899-6 | Excluded | Not a meta-analysis |
| Leppanen, J., Ng, K. W., Tchanturia, K., & Treasure, J. (2017). Meta-analysis of the effects of intranasal oxytocin on interpretation and expression of emotions. Neuroscience & Biobehavioral Reviews, 78, 125–144. doi:10.1016/j.neubiorev.2017.04.010 | Excluded | Only one study |
| Liang X, Li R, Wong SHS, et al. The Effects of Exercise Interventions on Executive Functions in Children and Adolescents with Autism Spectrum Disorder: A Systematic Review and Meta-analysis. Sports Med. 2022;52(1):75-88. doi:10.1007/s40279-021-01545-3 | Excluded | Outcome not included in the UR |
| Littell, J. H., Pigott, T. D., Nilsen, K. H., Green, S. J., & Montgomery, O. L. (2021). Multisystemic Therapy® for social, emotional, and behavioural problems in youth age 10 to 17: An updated systematic review and meta-analysis. Campbell Systematic Reviews, 17(4), e1158. | Excluded | Not on ASD |
| Liu, C., Li, T., Wang, Z., Zhou, R., & Zhuang, L. (2019). Scalp acupuncture treatment for children’s autism spectrum disorders. Medicine, 98(13), e14880. doi:10.1097/md.0000000000014880 | Excluded | Probable errors in calculations |
| Lowenthal 2019 | Excluded | Not enough information to replicate |
| Main, P. A., Angley, M. T., O’Doherty, C. E., Thomas, P., & Fenech, M. (2012). The potential role of the antioxidant and detoxification properties of glutathione in autism spectrum disorders: a systematic review and meta-analysis. Nutrition & metabolism, 9, 35. https://doi.org/10.1186/1743-7075-9-35 | Excluded | Not a meta-analysis of controlled studies |
| Maujean, A., Pepping, C. A., & Kendall, E. (2015). A systematic review of randomized controlled trials of animal-assisted therapy on psychosocial outcomes. Anthrozoös, 28(1), 23–36. https://doi.org/10.2752/089279315X14129350721812 | Excluded | Not a meta-analysis |
| McLay, L.-L. K., & France, K. (2014). Empirical research evaluating non-traditional approaches to managing sleep problems in children with autism. Developmental Neurorehabilitation, 1–12. doi:10.3109/17518423.2014.904452 | Excluded | Not a meta-analysis |
| Miyahara M. (2013). Meta review of systematic and meta analytic reviews on movement differences, effect of movement based interventions, and the underlying neural mechanisms in autism spectrum disorder. Frontiers in integrative neuroscience, 7, 16. | Excluded | Not a meta-analysis of controlled studies |
| Monteiro CE, Da Silva E, Sodré R, et al. The Effect of Physical Activity on Motor Skills of Children with Autism Spectrum Disorder: A Meta-Analysis. Int J Environ Res Public Health. 2022;19(21):14081. Published 2022 Oct 28. doi:10.3390/ijerph192114081 | Excluded | Outcome not included in the UR |
| Nimer, J., & Lundahl, B. (2007). Animal-assisted therapy: A meta-analysis. Anthrozoös, 20(3), 225–238. https://doi.org/10.2752/089279307X224773 | Excluded | Not on ASD |
| Nogueira HA, de Castro CT, da Silva DCG, Pereira M. Melatonin for sleep disorders in people with autism: Systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2023;123:110695. doi:10.1016/j.pnpbp.2022.110695 | Excluded | Probable errors in calculations |
| Nye, C., & Brice, A. (2005). Combined vitamin B6-magnesium treatment in autism spectrum disorder. Cochrane Database of Systematic Reviews. doi:10.1002/14651858.cd003497.pub2 | Excluded | Not a meta-analysis of controlled studies |
| Park, E. Y., Kim, W. H., & Blair, K. S. C. (2021). Effectiveness of interventions involving physical activities for individuals with autism spectrum disorder: a meta-analysis. Education and Training in Autism and Developmental Disabilities, 56(3), 354-367. | Excluded | Meta-analysis of SCD |
| Peled-Avron, L., Abu-Akel, A., & Shamay-Tsoory, S. (2020). Exogenous effects of oxytocin in five psychiatric disorders: a systematic review, meta-analysis and a personalized approach through the lens of the social salience hypothesis. Neuroscience & Biobehavioral Reviews. doi:10.1016/j.neubiorev.2020.04.023 | Excluded | Probable errors in calculations |
| Peng S, Fang Y, Othman AT, Liang J. Meta-analysis and systematic review of physical activity on neurodevelopment disorders, depression, and obesity among children and adolescents. Front Psychol. 2022;13:940977. Published 2022 Nov 30. doi:10.3389/fpsyg.2022.940977 | Excluded | Not enough information to replicate |
| Quan L, Xu X, Cui Y, et al. A systematic review and meta-analysis of the benefits of a gluten-free diet and/or casein-free diet for children with autism spectrum disorder. Nutr Rev. 2022;80(5):1237-1246. doi:10.1093/nutrit/nuab073 | Excluded | Pre/post effect sizes within the experimental group |
| Rehn AK, Caruso VR, Kumar S. The effectiveness of animal-assisted therapy for children and adolescents with autism spectrum disorder: A systematic review. Complement Ther Clin Pract. 2023;50:101719. doi:10.1016/j.ctcp.2022.101719 | Excluded | Not a meta-analysis |
| Romero-Martínez Á, Bressanutti S, Moya-Albiol L. A Systematic Review of the Effectiveness of Non-Invasive Brain Stimulation Techniques to Reduce Violence Proneness by Interfering in Anger and Irritability. J Clin Med. 2020;9(3):882. Published 2020 Mar 24. doi:10.3390/jcm9030882 | Excluded | Not a meta-analysis |
| Rossignol DA, Frye RE. Melatonin in autism spectrum disorders: a systematic review and meta-analysis. Dev Med Child Neurol. 2011;53(9):783-792. doi:10.1111/j.1469-8749.2011.03980.x | Excluded | Probable errors in calculations |
| Rossignol DA, Frye RE. The Effectiveness of Cobalamin (B12) Treatment for Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. J Pers Med. 2021;11(8):784. Published 2021 Aug 11. doi:10.3390/jpm11080784 | Excluded | Only one study |
| Rossignol, D. A., & Frye, R. E. (2021). Cerebral Folate Deficiency, Folate Receptor Alpha Autoantibodies and Leucovorin (Folinic Acid) Treatment in Autism Spectrum Disorders: A Systematic Review and Meta-Analysis. Journal of personalized medicine, 11(11), 1141. https://doi.org/10.3390/jpm11111141 | Excluded | Not a meta-analysis of controlled studies |
| Ruan, H., Eungpinichpong, W., Wu, H., Shen, M., & Zhang, A. (2022). Medicine Insufficient Evidence for the Efficacy of Massage as Intervention for Autism Spectrum Disorder: A Systematic Review. Evidence-based complementary and alternative medicine : eCAM, 2022, 5328320. | Excluded | Not a meta-analysis |
| Salehi A, Hashemi N, Imanieh MH, Saber M. Chiropractic: Is it Efficient in Treatment of Diseases? Review of Systematic Reviews. Int J Community Based Nurs Midwifery. 2015;3(4):244-254. | Excluded | Not a meta-analysis of controlled studies |
| Sefen, J. A. N., Al-Salmi, S., Shaikh, Z., AlMulhem, J. T., Rajab, E., & Fredericks, S. (2020). Beneficial Use and Potential Effectiveness of Physical Activity in Managing Autism Spectrum Disorder. Frontiers in Behavioral Neuroscience, 14. doi:10.3389/fnbeh.2020.587560 | Excluded | Not a meta-analysis |
| Shi, Z. M., Lin, G. H., & Xie, Q. (2016). Effects of music therapy on mood, language, behavior, and social skills in children with autism: A meta-analysis. Chinese Nursing Research, 3(3), 137-141. | Excluded | Probable errors in calculations |
| Shuai B, Jin H, Lin Y, et al. Safety and efficacy of complementary and alternative medicine in the treatment of autism spectrum disorder: A protocol for systematic review and meta-analysis. Medicine (Baltimore). 2020;99(45):e23128. doi:10.1097/MD.0000000000023128 | Excluded | Protocol |
| Shuai B, Jin H, Lin Y, et al. Safety and efficacy of complementary and alternative medicine in the treatment of autism spectrum disorder: A protocol for systematic review and meta-analysis. Medicine (Baltimore). 2020;99(45):e23128. doi:10.1097/MD.0000000000023128 | Excluded | Protocol |
| Silva EAD Junior, Medeiros WMB, Torro N, et al. Cannabis and cannabinoid use in autism spectrum disorder: a systematic review. Trends Psychiatry Psychother. 2022;44:e20200149. Published 2022 Jun 13. doi:10.47626/2237-6089-2020-0149 | Excluded | Not a meta-analysis |
| Smith JR, DiSalvo M, Green A, et al. Treatment Response of Transcranial Magnetic Stimulation in Intellectually Capable Youth and Young Adults with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis [published online ahead of print, 2022 Sep 26]. Neuropsychol Rev. 2022;10.1007/s11065-022-09564-1. doi:10.1007/s11065-022-09564-1 | Excluded | Pre/post effect sizes within the experimental group |
| Sowa, M., & Meulenbroek, R. (2012). Effects of physical exercise on autism spectrum disorders: A meta-analysis. Research in autism spectrum disorders, 6(1), 46-57. | Excluded | Pre/post effect sizes within the experimental group |
| Sun CK, Cheng YS, Hung KC. N-acetylcysteine is effective as add-on therapy to risperidone-based combination for children with autistic disorders. Aust N Z J Psychiatry. 2022;56(1):91-92. doi:10.1177/00048674211041932 | Excluded | Comment |
| Sung MC, Ku B, Leung W, MacDonald M. The Effect of Physical Activity Interventions on Executive Function Among People with Neurodevelopmental Disorders: A Meta-Analysis. J Autism Dev Disord. 2022;52(3):1030-1050. doi:10.1007/s10803-021-05009-5 | Excluded | Outcome not included in the UR |
| Tan, B. W. Z., Pooley, J. A., & Speelman, C. P. (2016). A Meta-Analytic Review of the Efficacy of Physical Exercise Interventions on Cognition in Individuals with Autism Spectrum Disorder and ADHD. Journal of Autism and Developmental Disorders, 46(9), 3126–3143. doi:10.1007/s10803-016-2854-x | Excluded | Outcome not included in the UR |
| Tarr, C. W., Rineer-Hershey, A., & Larwin, K. (2020). The effects of physical exercise on stereotypic behaviors in autism: Small-n meta-analyses. Focus on Autism and Other Developmental Disabilities, 35(1), 26-35. | Excluded | Not enough information to replicate |
| Teh, E. J., Vijayakumar, R., Tan, T. X. J., & Yap, M. J. (2022). Effects of Physical Exercise Interventions on Stereotyped Motor Behaviours in Children with ASD: A Meta-Analysis. Journal of autism and developmental disorders, 52(7), 2934–2957. | Excluded | Probable errors in calculations |
| Trzmiel, T., Purandare, B., Michalak, M., Zasadzka, E., & Pawlaczyk, M. (2019). Equine assisted activities and therapies in children with autism spectrum disorder: A systematic review and a meta-analysis. Complementary therapies in medicine, 42, 104–113. | Excluded | Pre/post effect sizes within the experimental group |
| Vancampfort D, Scheewe T, van Damme T, Deenik J. Effect van bewegen op psychiatrische symptomen en lichamelijke gezondheid bij mensen met psychiatrische aan-doeningen; systematische review van recente meta-analyses [The efficacy of physical activity on psychiatric symptoms and physical health in people with psychiatric disorders: a systematic review of recent meta-analyses]. Tijdschr Psychiatr. 2020;62(11):936-945. | Excluded | Not a meta-analysis |
| Varigonda, A. L., Edgcomb, J. B., & Zima, B. T. (2021). The impact of exercise in improving executive function impairments among children and adolescents with ADHD, autism spectrum disorder, and fetal alcohol spectrum disorder: a systematic review and meta-analysis. Archives of Clinical Psychiatry (São Paulo), 47, 146-156. | Excluded | Pre/post effect sizes within the experimental group |
| Wang, L., Peng, J. L., Qiao, F. Q., Cheng, W. M., Lin, G. W., Zhang, Y., Gao, T. G., Sun, Y. Y., Tang, W. Z., & Wang, P. (2021). Clinical Randomized Controlled Study of Acupuncture Treatment on Children with Autism Spectrum Disorder (ASD): A Systematic Review and Meta-Analysis. Evidence-based complementary and alternative medicine : eCAM, 2021, 5549849. | Excluded | Probable errors in calculations |
| Whipple, J. (2004). Music in Intervention for Children and Adolescents with Autism: A Meta-Analysis. Journal of Music Therapy, 41(2), 90–106. doi:10.1093/jmt/41.2.90 | Excluded | Meta-analysis of SCD |
| Wigton, R., Radua, J., Allen, P., Averbeck, B., Meyer-Lindenberg, A., McGuire, P., Shergill, S. S., & Fusar-Poli, P. (2015). Neurophysiological effects of acute oxytocin administration: systematic review and meta-analysis of placebo-controlled imaging studies. Journal of psychiatry & neuroscience : JPN, 40(1), E1–E22 | Excluded | Not a meta-analysis |
| Williams, K. J., Wray, J. J., & Wheeler, D. M. (2005). Intravenous secretin for autism spectrum disorder. Cochrane Database of Systematic Reviews. doi:10.1002/14651858.cd003495.pub2 | Excluded | Updated later |
| Williams, K., Wray, J. A., & Wheeler, D. M. (2012). Intravenous secretin for autism spectrum disorders (ASD). The Cochrane database of systematic reviews, 2012(4), CD003495. | Excluded | Not a meta-analysis |
| Xiao N, Shinwari K, Kiselev S, Huang X, Li B, Qi J. Effects of Equine-Assisted Activities and Therapies for Individuals with Autism Spectrum Disorder: Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2023 Feb 1;20(3):2630. doi: 10.3390/ijerph20032630. PMID: 36767996; PMCID: PMC9915993. | Excluded | Not enough information to replicate |
| Yang, Y. J., Siao, M. R., Tsai, F. T., & Luo, H. J. (2015). Effect of physical activity interventions on children and adolescents with autism spectrum disorder: a systematic review and meta-analysis. Physiotherapy, 101, e1685-e1686. | Excluded | Not enough information to replicate |
| Yi et al 2020 IOP Conf. Ser.: Earth Environ. Sci. 440 042094 | Excluded | Probable errors in calculations |
| Zhang, M., Liu, Z., Ma, H., & Smith, D. M. (2020). Chronic Physical Activity for Attention Deficit Hyperactivity Disorder and/or Autism Spectrum Disorder in Children: A Meta-Analysis of Randomized Controlled Trials. Frontiers in behavioral neuroscience, 14, 564886. | Excluded | Outcome not included in the UR |
| Zhang, Y., Zeng, J., Wu, D., Li, X., Chen, Y., Dai, S., Wang, B., Qi, Y., & Lu, J. (2021). Effect and safety of acupuncture for autism spectrum disorders: A protocol for systematic review and meta-analysis. Medicine, 100(11), e22269. | Excluded | Protocol |
| Zhao M, Chen S, You Y, Wang Y, Zhang Y. Effects of a Therapeutic Horseback Riding Program on Social Interaction and Communication in Children with Autism. International Journal of Environmental Research and Public Health. 2021; 18(5):2656 | Excluded | Only one study |
| Zhukova, M. A., Talantseva, O. I., Logvinenko, T. I., Titova, O. S., & Grigorenko, E. L. (2020). Complementary and Alternative Treatments for Autism Spectrum Disorders: A Review for Parents and Clinicians. Clinical Psychology & Special Education/Kliniceska I Special’naa Psihologia, 9(3). | Excluded | Not a meta-analysis of controlled studies |
| NA | Excluded | Not a meta-analysis |
| NA | Excluded | Comment |
| NA | Excluded | Not a meta-analysis |
| NA | Excluded | Meta-analysis of SCD |
| NA | Excluded | Not a meta-analysis |
| NA | Excluded | Not on ASD |
| NA | Excluded | Not a meta-analysis |
| NA | Excluded | Not a meta-analysis |
| NA | Excluded | Not a meta-analysis |
| Nuria Prades, Eva Varela, Itziar Flamarique, Ramon Deulofeu & Inmaculada Baeza (2023) Water-soluble vitamin insufficiency, deficiency and supplementation in children and adolescents with a psychiatric disorder: a systematic review and meta-analysis, Nutritional Neuroscience, 26:2, 85-107, DOI: 10.1080/1028415X.2021.2020402 | Excluded | Not a meta-analysis of controlled studies |
| Soares A, Shiozawa P, Trevizol AP, Paula CS, Lowenthal R, Cordeiro Q. Effects of augmentation agents in autistic disorder patients treated with risperidone: a systematic review and a meta-analysis. Trends Psychiatry Psychother. 2016;38(2):114-116. doi:10.1590/2237-6089-2015-0068 | Excluded | Mixed intervention types |
| Soares, A., Shiozawa, P., Trevizol, A. P., Paula, C. S. D., Lowenthal, R., & Cordeiro, Q. (2016). Effects of augmentation agents in autistic disorder patients treated with risperidone: a systematic review and a meta-analysis. Trends in psychiatry and psychotherapy, 38, 114-116. | Excluded | Mixed intervention types |
| Yu Z, Zhang P, Tao C, Lu L, Tang C. Efficacy of nonpharmacological interventions targeting social function in children and adults with autism spectrum disorder: A systematic review and meta-analysis. PLoS One. 2023;18(9):e0291720. Published 2023 Sep 19. doi:10.1371/journal.pone.0291720 | Excluded | Probable errors in calculations |
| Sam, K. L., Chow, B. C., & Tong, K. K. (2015). Effectiveness of exercise-based interventions for children with autism: A systematic review and meta-analysis. International Journal of Learning and Teaching, 1(2), 98-103. | Excluded | Mixed intervention types |
| Lun T, Lin S, Chen Y, et al. Acupuncture for children with autism spectrum disorder: A systematic review and meta-analysis. Medicine (Baltimore). 2023;102(8):e33079. doi:10.1097/MD.0000000000033079 | Excluded | Outcome not included in the UR |
| Beavers A, Fleming A, Shahidullah JD. Animal-assisted therapies for autism. Curr Probl Pediatr Adolesc Health Care. 2023;53(11):101478. doi:10.1016/j.cppeds.2023.101478 | Excluded | Not a meta-analysis of controlled studies |
| Lavín-Pérez AM, Rivera-Martín B, Lobato-Rincón LL, Villafaina-Domínguez S, Collado-Mateo D. Benefits of animal-Assisted interventions in preschool children: A systematic review. Clin Child Psychol Psychiatry. 2023;28(2):850-873. doi:10.1177/13591045221142115 | Excluded | Not a meta-analysis of controlled studies |
| Michele Mussap1 , Rossella Tomaiuolo2 New insights on biomarkers reflecting the genetic deficiency of folate cycle in autism spectrum disorder Biochimica Clinica 2023; 47(2) 125-126 | Excluded | Not a meta-analysis of controlled studies |
| JAMEY https://www.biorxiv.org/content/10.1101/2023.02.08.527718v2.full.pdf | Excluded | Not a meta-analysis of controlled studies |
| Zhang J, Zhu G, Wan L, et al. Effect of fecal microbiota transplantation in children with autism spectrum disorder: A systematic review. Front Psychiatry. 2023;14:1123658. Published 2023 Mar 2. doi:10.3389/fpsyt.2023.1123658 | Excluded | Not a meta-analysis |
| Ji YQ, Tian H, Zheng ZY, Ye ZY, Ye Q. Effectiveness of exercise intervention on improving fundamental motor skills in children with autism spectrum disorder: a systematic review and meta-analysis. Front Psychiatry. 2023;14:1132074. Published 2023 Jun 12. doi:10.3389/fpsyt.2023.1132074 | Excluded | Outcome not included in the UR |
| Rice LJ, Cannon L, Dadlani N, et al. Efficacy of cannabinoids in neurodevelopmental and neuropsychiatric disorders among children and adolescents: a systematic review. Eur Child Adolesc Psychiatry. Published online March 3, 2023. doi:10.1007/s00787-023-02169-w | Excluded | Not a meta-analysis |
| Zhang J, Zhu G, Wan L, et al. Effect of fecal microbiota transplantation in children with autism spectrum disorder: A systematic review. Front Psychiatry. 2023;14:1123658. Published 2023 Mar 2. doi:10.3389/fpsyt.2023.1123658 | Excluded | Not a meta-analysis of controlled studies |
| Lewandowska-Pietruszka Z, Figlerowicz M, Mazur-Melewska K. Microbiota in Autism Spectrum Disorder: A Systematic Review. Int J Mol Sci. 2023;24(23):16660. Published 2023 Nov 23. doi:10.3390/ijms242316660 | Excluded | Not a meta-analysis of controlled studies |
| Fan MSN, Li WHC, Ho LLK, Phiri L, Choi KC. Nature-Based Interventions for Autistic Children: A Systematic Review and Meta-Analysis. JAMA Netw Open. 2023;6(12):e2346715. Published 2023 Dec 1. doi:10.1001/jamanetworkopen.2023.46715 | Excluded | Mixed intervention types |
| Wong T, Falcomata TS, Barnett M. The Collateral Effects of Antecedent Exercise on Stereotypy and Other Nonstereotypic Behaviors Exhibited by Individuals with Autism Spectrum Disorder: A Systematic Review. Behav Anal Pract. 2022;16(2):407-420. Published 2022 Sep 19. doi:10.1007/s40617-022-00746-0 | Excluded | Not a meta-analysis |
| Li, Y., Feng, Y., Zhong, J. et al. The Effects of Physical Activity Interventions in Children with Autism Spectrum Disorder: a Systematic Review and Network Meta-analysis. Rev J Autism Dev Disord (2023). https://doi.org/10.1007/s40489-023-00418-x | Excluded | Not enough information to replicate |
| Ye, Y.; Ning, K.; Wan, B.; Shangguan, C. The Effects of the Exercise Intervention on Fundamental Movement Skills in Children with Attention Deficit Hyperactivity Disorder and/or Autism Spectrum Disorder: A Meta-Analysis. Sustainability 2023, 15, 5206. https://doi.org/10.3390/ su15065206 | Excluded | Outcome not included in the UR |
| NA | Excluded | Not a meta-analysis of controlled studies |
| Xiong M, Li F, Liu Z, et al. Efficacy of Melatonin for Insomnia in Children with Autism Spectrum Disorder: A Meta-analysis. Neuropediatrics. 2023;54(3):167-173. doi:10.1055/s-0043-1761437 | Excluded | Probable errors in calculations |
| Nogueira HA, de Castro CT, da Silva DCG, Pereira M. Melatonin for sleep disorders in people with autism: Systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2023;123:110695. doi:10.1016/j.pnpbp.2022.110695 | Excluded | Probable errors in calculations |
| NA | Excluded | Not a meta-analysis of controlled studies |
| NA | Excluded | Retracted |
| Kisely S, Connor M, Somogyi AA, Siskind D. A systematic literature review and meta-analysis of the effect of psilocybin and methylenedioxymethamphetamine on mental, behavioural or developmental disorders. Aust N Z J Psychiatry. 2023;57(3):362-378. doi:10.1177/00048674221083868 | Excluded | Only one study |
| Parrella NF, Hill AT, Enticott PG, Barhoun P, Bower IS, Ford TC. A systematic review of cannabidiol trials in neurodevelopmental disorders. Pharmacol Biochem Behav. 2023;230:173607. doi:10.1016/j.pbb.2023.173607 | Excluded | Not a meta-analysis of controlled studies |
| Lin X, Wang G, Shen S, Zhan J. Advances in the Diagnosis and Treatment of Autism Spectrum Disorders in Children. Altern Ther Health Med. Published online October 27, 2023. | Excluded | Not a meta-analysis of controlled studies |
tab_excluded = res_excluded %>%
group_by(Reasons_exclusion) %>%
summarise(n=n()) %>%
arrange(n)
paste0("n-eligible = ", nrow(res_included) + sum(tab_excluded$n[tab_excluded$Reasons_exclusion %in% c("Probable errors in calculations", "Not enough information to replicate")]))## [1] "n-eligible = 72"tab_excluded## # A tibble: 15 x 2
## Reasons_exclusion n
## <chr> <int>
## 1 Comment 3
## 2 Mixed outcomes 3
## 3 Meta-analysis of SCD 4
## 4 Only one study 4
## 5 Retracted 4
## 6 Updated later 4
## 7 Mixed intervention types 7
## 8 Protocol 7
## 9 Not enough information to replicate 8
## 10 Outcome not included in the UR 9
## 11 Pre/post effect sizes within the experimental group 9
## 12 Not on ASD 10
## 13 Probable errors in calculations 12
## 14 Not a meta-analysis of controlled studies 37
## 15 Not a meta-analysis 39S11. Primary analysis
SX-1. Data analysis
Overal scoring
res_m$Age = factor(res_m$Age,
levels=c('< 6 yo', '6-12 yo', '13-19 yo', '>= 20 yo'))
res_m$GRADE = factor(res_m$GRADE,
levels=c('High', 'Moderate', 'Low', 'Very low'))
res_m = res_m %>%
mutate(Outcome_rank = factor(res_m$Outcome,
levels = c(
"Overall ASD symptoms",
"Social-communication", "Restricted/repetitive behaviors",
"Sensory Profile", "Acceptability",
"Tolerability", "Adverse events",
"Global cognition (IQ)",
"Adaptive behaviors",
"Quality of life",
"Language (Expressive skills)",
"Language (Receptive skills)",
"Language (Overall skills)",
"ADHD symptoms", "Anxiety", "Mood related symptoms",
"Disruptive behaviors", "Sleep quality", "Sleep quantity")),
age_rank = factor(age_pre, levels = c(
"Pre-school (<6 years old)",
"School-age (6-12 years old)",
"Adolescents (13-19 years old)",
"Adults (>=20 years old)"
)),
col_sig = case_when(
eG <= -0.8 ~ "#DC5746",
eG <= -0.5 & eG > -0.8 ~ "#DD8378",
eG <= -0.2 & eG > -0.5 ~ "#DEADA7",
eG < 0.2 & eG > -0.2 ~ "#D4D4D4",
eG >= 0.2 & eG < 0.5 ~ "#A2CDAE",
eG >= 0.5 & eG < 0.8 ~ "#68CD84",
eG >= 0.8 ~ "#30CC5C"),
GRADE_rank = case_when(
GRADE == "Very low" ~ 0,
GRADE == "Low" ~ 1,
GRADE == "Moderate" ~ 3,
GRADE == "High" ~ 4
),
col_contour = case_when(
GRADE == "Very low" ~ "transparent",
GRADE != "Very low" ~ "black"
),
Intervention_rank = case_when(
Intervention %in% Mind_body_medicine ~ "Mind-body",
Intervention %in% therapies_energetiques ~ "Energetic",
Intervention %in% Natural_Product_Based_Therapies ~ "Natural",
Intervention %in% systemes_medicaux_alternatifs ~ "Alternative"
)) %>%
arrange(Age, Intervention, GRADE, eG)
colSums(res_m[,
c("down_rob", "down_het", "down_ind",
"down_imp", "down_pubbias")] == 2)## down_rob down_het down_ind down_imp down_pubbias
## 91 88 0 112 0colSums(res_m[,
c("down_rob", "down_het", "down_ind",
"down_imp", "down_pubbias")] == 1)## down_rob down_het down_ind down_imp down_pubbias
## 56 37 80 127 164res_m$paper = gsub("\\(child\\) ", "", res_m$paper)
res_m$paper = gsub("\\(adult\\) ", "", res_m$paper)
res_m$n_paper = length(unique(paste0(res_m$paper)))interventions = sort(unique(res_m$intervention_general))
outcomes = sort(unique(res_m$outcome_general)); interventions; outcomes## [1] "AAI" "ACUP" "DIET" "HERB" "L-CARNIT" "L-CARNO"
## [7] "MELAT" "MUSIC" "NAC" "OXYT" "PHYS" "PROB"
## [13] "PUFA" "rTMS" "SECRET" "SENS" "SULFO" "tDCS"
## [19] "VIT-D"## [1] "Acceptability" "Adaptive behaviors"
## [3] "ADHD symptoms" "Adverse events"
## [5] "Anxiety" "Disruptive behaviors"
## [7] "Global cognition (IQ)" "Language (Expressive skills)"
## [9] "Language (Overall skills)" "Language (Receptive skills)"
## [11] "Mood related symptoms" "Overall ASD symptoms"
## [13] "Quality of life" "Restricted/repetitive behaviors"
## [15] "Sensory Profile" "Sleep quality"
## [17] "Sleep quantity" "Social-communication"
## [19] "Tolerability"Primary analysis
SX-1. Synthesis - table
res_p = res_m %>% filter(IN_meta == 1)
rio::export(res_m, paste0(chemin, "UR_CAM_analysis.xlsx"), overwrite = TRUE)
# View(res_p %>%
# group_by(PICO_amstar) %>%
# summarise(n = n()))
DT::datatable(res_p,
rownames = FALSE,
extensions = 'Buttons',
class = "display",
options = list(
# dom = c('t'),
scrollX = TRUE,
scrollCollapse = TRUE,
scrollY = "500px",
pageLength = nrow(res_p),
columnDefs = list(
list(width = '100px',
targets = "_all"),
list(className = 'dt-center',
targets = "_all")),
dom = c('tB'),
buttons = c('copy', 'csv', 'excel','pdf')
))# res_p %>%
# filter(GRADE %in% c("Low", "Moderate") ) %>%
# select(PICO_amstar, GRADE, Age, eG, p_value) %>%
# arrange(Age, GRADE)
# res_m %>%
# filter(Intervention == "OXYT" & Outcome == "Restricted/repetitive behaviors") %>%
# select(PICO_amstar, GRADE, Age, eG, p_value, paper) %>%
# arrange(Age, GRADE)SX-1. Synthesis - Plots
FIG 2.a
res_p$intervention_spell = factor (res_p$intervention_spell)
res_p$intervention_spell <- factor(res_p$intervention_spell, levels = sort(unique(as.character(res_p$intervention_spell)), decreasing = TRUE))
res_sum = res_p %>%
filter(Outcome %in% c(ASD_symptoms, safety))
ggplot(res_sum, aes(Outcome_rank, intervention_spell)) +
geom_point(shape = 21, size = 5,
fill="transparent",
aes(stroke = GRADE_rank,
color = col_contour)) +
geom_point(shape = 21, aes(size = n_studies,
fill = col_sig,
color = col_sig)) +
geom_point(data = res_sum %>% filter(as.numeric(p_value) < 0.05),
aes(Outcome_rank, intervention_spell),
colour = "#000000",
size = 2,
shape=8) +
theme_bw() +
facet_grid(.~ age_rank, switch = "x") +
theme(axis.ticks.x=element_blank(),
axis.text.x=element_text(size = 7, angle = 55, hjust = 0),
legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank())+
scale_x_discrete(position = "top",
expand = expansion(mult = c(0.09, 0.09))) +
scale_y_discrete(expand = expansion(mult = c(0.05, 0.05)), drop = FALSE) +
scale_size_continuous(range = c(3, 5.5)) +
theme(plot.margin = unit(c(10,10,10,10), "mm"))+
scale_fill_manual(values = c("#DC5746" = "#DC5746", "#DD8378" = "#DD8378", "#DEADA7" = "#DEADA7", "#D4D4D4" = "#D4D4D4", "#A2CDAE" = "#BCEBBC", "#68CD84" = "#73C289", "#30CC5C" = "#09A302", "white" = "white", "transparent" = "transparent", "black" = "black")) +
scale_colour_manual(values = c("#DC5746" = "#DC5746", "#DD8378" = "#DD8378", "#DEADA7" = "#DEADA7", "#D4D4D4" = "#D4D4D4", "#A2CDAE" = "#BCEBBC", "#68CD84" = "#73C289", "#30CC5C" = "#09A302", "white" = "white", "transparent" = "transparent", "black" = "black", small="black", large="white")) 
res_sum_add = res_p %>%
filter(!Outcome %in% c(ASD_symptoms, safety))
ggplot(res_sum_add, aes(Outcome_rank, intervention_spell)) +
geom_point(shape = 21, size = 5.5,
fill="transparent",
aes(stroke = GRADE_rank,
color = col_contour)) +
geom_point(shape = 21, aes(size = n_studies,
fill = col_sig,
color = col_sig)) +
geom_point(data = . %>% filter(as.numeric(p_value) < 0.05),
aes(Outcome_rank, intervention_spell),
colour = "#000000",
size = 2,
shape=8) +
theme_bw() +
facet_grid(.~ age_rank, switch = "x") +
theme(axis.ticks.x=element_blank(),
axis.text.x=element_text(size = 7, angle = 55, hjust = 0),
legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank())+
scale_x_discrete(position = "top",
expand = expansion(mult = c(0.09, 0.09))) +
scale_y_discrete(expand = expansion(mult = c(0.05, 0.05)), drop = FALSE) +
scale_size_continuous(range = c(3, 5)) +
theme(plot.margin = unit(c(10,10,10,10), "mm"))+
scale_fill_manual(values = c("#DC5746" = "#DC5746", "#DD8378" = "#DD8378", "#DEADA7" = "#DEADA7", "#D4D4D4" = "#D4D4D4", "#A2CDAE" = "#BCEBBC", "#68CD84" = "#73C289", "#30CC5C" = "#09A302", "white" = "white", "transparent" = "transparent", "black" = "black")) +
scale_colour_manual(values = c("#DC5746" = "#DC5746", "#DD8378" = "#DD8378", "#DEADA7" = "#DEADA7", "#D4D4D4" = "#D4D4D4", "#A2CDAE" = "#BCEBBC", "#68CD84" = "#73C289", "#30CC5C" = "#09A302", "white" = "white", "transparent" = "transparent", "black" = "black", small="black", large="white")) 
res_sum_school = res_p %>%
filter(Age == "6-12 yo")
ggplot(res_sum_school, aes(Outcome_rank, Intervention)) +
geom_point(shape = 21, size = 5,
fill="transparent",
aes(stroke = GRADE_rank,
color = col_contour)) +
geom_point(shape = 21, aes(size = n_studies,
fill = col_sig,
color = col_sig)) +
geom_point(data = . %>% filter(as.numeric(p_value) < 0.05),
aes(Outcome_rank, Intervention),
colour = "#000000",
size = 2,
shape=8) +
theme_bw() +
facet_grid(.~ age_rank, switch = "x") +
theme(axis.ticks.x=element_blank(),
axis.text.x=element_text(size = 7, angle = 55, hjust = 0),
legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank())+
scale_x_discrete(position = "top",
expand = expansion(mult = c(0.09, 0.09))) +
scale_y_discrete(expand = expansion(mult = c(0.05, 0.05))) +
scale_size_continuous(range = c(3, 5.5)) +
theme(plot.margin = unit(c(10,10,10,10), "mm"))+
scale_fill_manual(values = c("#DC5746" = "#DC5746", "#DD8378" = "#DD8378", "#DEADA7" = "#DEADA7", "#D4D4D4" = "#D4D4D4", "#A2CDAE" = "#BCEBBC", "#68CD84" = "#73C289", "#30CC5C" = "#09A302", "white" = "white", "transparent" = "transparent", "black" = "black")) +
scale_colour_manual(values = c("#DC5746" = "#DC5746", "#DD8378" = "#DD8378", "#DEADA7" = "#DEADA7", "#D4D4D4" = "#D4D4D4", "#A2CDAE" = "#BCEBBC", "#68CD84" = "#73C289", "#30CC5C" = "#09A302", "white" = "white", "transparent" = "transparent", "black" = "black", small="black", large="white")) 
Fig 2. — Low or Moderate GRADE rating
dat_low_or_higher = res_p %>% filter(GRADE %in% c("Low", "Moderate")) %>%
arrange(Age, GRADE, eG)
metaumbrella::forest(as.data.frame(dat_low_or_higher),
layout = "RevMan5",
subgroup = "age_rank",
subgroup.name = "",
leftcols = c("Intervention", "Outcome", "GRADE",
"n_studies", "rob_num", "I2", "effect.ci"),
leftlabs = c("Intervention", "Outcome", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Low or Moderate GRADE"
)
Core symptoms
res_core = res_p %>% filter(Outcome %in% ASD_symptoms)
metaumbrella::forest(as.data.frame(res_core),
layout = "RevMan5",
subgroup = "Outcome",
subgroup.name = "",
leftcols = c("Intervention", "Age", "GRADE",
"n_studies", "rob_num", "I2", "effect.ci"),
leftlabs = c("Intervention", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
# sortvar = res_p$Age,
smlab = "Core ASD symptoms"
)
Safety
Fig 3. — Safety outcomes
forest(res_p %>% filter(Outcome %in% safety) %>%
arrange(Age, GRADE, eG) %>%
as.data.frame(),
title = "equivalent Risk Ratio (eRR)",
xlim = c(0.1, 10),
squaresize = 0.7,
weight.study = "same",
layout = "RevMan5",
subgroup = "Outcome",
xlab = "Equivalent Risk Ratio (eRR)",
subgroup.name = "",
leftcols = c("Intervention", "Age", "GRADE",
"n_studies", "rob_num", "I2", "effect.ci"),
leftlabs = c("Intervention", "Age", "GRADE",
"n-studies", "Low\nRoB", "I²", "eRR + 95% CI"),
# sortvar = res_p$Age,
smlab = "Safety"
)
Functioning
forest(res_p %>% filter(Outcome %in% functioning) %>%
as.data.frame(),
layout = "RevMan5",
subgroup = "Outcome",
subgroup.name = "",
leftcols = c("Intervention", "Age", "GRADE",
"n_studies", "rob_num", "I2", "effect.ci"),
leftlabs = c("Intervention", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
# sortvar = res_p$Age,
smlab = "Daily functioning"
)
Comorbidities
forest(res_p %>% filter(Outcome %in% comorbidities) %>%
as.data.frame(),
layout = "RevMan5",
subgroup = "Outcome",
subgroup.name = "",
leftcols = c("Intervention", "Age", "GRADE",
"n_studies", "rob_num", "I2", "effect.ci"),
leftlabs = c("Intervention", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
# sortvar = res_p$Age,
smlab = "Psychiatric comorbidities"
)
ASD-related symptoms
forest(res_p %>% filter(Outcome %in% ASD_related) %>%
as.data.frame(),
layout = "RevMan5",
subgroup = "Outcome",
subgroup.name = "",
leftcols = c("Intervention", "Age", "GRADE",
"n_studies", "rob_num", "I2", "effect.ci"),
leftlabs = c("Intervention", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
# sortvar = res_p$Age,
smlab = "ASD-related symptoms"
)
S12. Overlapping
Overall picture
# Function to calculate interval overlap
interval_overlap_percentage <- function(lower1, upper1, lower2, upper2) {
overlap = max(0, min(upper1, upper2) - max(lower1, lower2))
total_length = (upper1 - lower1) + (upper2 - lower2) - overlap
percentage_overlap = (overlap / total_length) * 100
return(percentage_overlap)
}
res_over <- res_m %>%
group_by(PICO_amstar) %>%
filter(n() > 1) %>%
mutate(n_SRMA = length(unique(paper))) %>%
group_by(PICO_amstar) %>%
mutate(
eG_meta_IN = eG[IN_meta == 1],
GRADE_meta_IN = GRADE[IN_meta == 1],
p_value_meta_IN = p_value[IN_meta == 1],
n = n(),
n_SRMA = unique(n_SRMA),
paper = collapsunique(paper),
min_es = min(eG),
max_es = max(eG),
dif_es = max(eG) - min(eG),
max_grade = head(sort(GRADE), 1),
min_grade = tail(sort(GRADE), 1),
prop_sig = sum(as.numeric(p_value) < 0.05) / n(),
avg_percentage_overlap = mean(map_dbl(combn(n(), 2, simplify = FALSE), function(idx) {
interval_overlap_percentage(
ci_lo_g[idx[1]], ci_up_g[idx[1]],
ci_lo_g[idx[2]], ci_up_g[idx[2]])
})),
min_percentage_overlap = min(map_dbl(combn(n(), 2, simplify = FALSE), function(idx) {
interval_overlap_percentage(
ci_lo_g[idx[1]], ci_up_g[idx[1]],
ci_lo_g[idx[2]], ci_up_g[idx[2]])
}))
) %>%
arrange(PICO_amstar)
# overlap_factors_disc = res_over %>%
# mutate(
# discrepancy = case_when(
# abs(as.numeric(GRADE_meta_IN) - as.numeric(GRADE)) >= 2 ~ "GRADE",
# (abs(eG_meta_IN - eG) >= 0.30 &
# sum(as.numeric(p_value_meta_IN) < 0.05,
# as.numeric(p_value) < 0.05) == 1) ~ "ES",
# as.numeric(avg_percentage_overlap) < 20 ~ "95 CI")) %>%
# select(PICO_amstar, Factor, discrepancy, IN_meta) %>%
# filter(!is.na(discrepancy)) %>%
# group_by(PICO_amstar) %>%
# summarise( collapsunique(discrepancy))
overlap_factors_disc = res_over %>%
# filter((PICO_hom & Age_precise == "Homogeneous") | !PICO_hom | IN_meta == 1) %>%
filter(abs(as.numeric(GRADE_meta_IN) - as.numeric(GRADE)) >= 2 |
(abs(eG_meta_IN - eG) >= 0.30 &
sum(as.numeric(p_value_meta_IN) < 0.05,
as.numeric(p_value) < 0.05) == 1) |
as.numeric(avg_percentage_overlap) < 20) %>%
select(PICO_amstar, Factor, IN_meta)
res_over_disc = res_over %>%
filter(PICO_amstar %in% overlap_factors_disc$PICO_amstar)
propC = function(x, R=0) {
x_non_na = x[!is.na(x)]
round(sum(x_non_na)/length(x) * 100, R)
}
DT::datatable(res_over)# identify, for each Fig 4. Overlap
res_m$inter_out = paste0(res_m$Intervention, " - ", res_m$Outcome)
metaumbrella::forest(res_m %>% filter(Factor %in% res_over_disc$Factor) %>%
as.data.frame(),
layout = "RevMan5",
squaresize = 0.7,
weight.study = "same",
subgroup = "inter_out",
subgroup.name = "",
leftcols = c("paper", "Age", "GRADE",
"n_studies", "rob_num", "I2", "effect.ci"),
leftlabs = c("Paper", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
# sortvar = res_p$Age,
smlab = "Key overlapping situations"
)
Mind-Body Medicine
MUSIC
forest(res_m %>% filter(Intervention %in% "MUSIC") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR (key items)", "AMSTAR (Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (MUSIC)")
SENS
forest(res_m %>% filter(Intervention %in% "SENS") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR (key items)", "AMSTAR (Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (SENS)")
PHYS
forest(res_m %>% filter(Intervention %in% "PHYS") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR (key items)", "AMSTAR (Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (PHYS)")
rTMS
forest(res_m %>% filter(Intervention %in% "rTMS") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR (key items)", "AMSTAR (Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (rTMS)")
tCDS
forest(res_m %>% filter(Intervention %in% "tDCS") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (tDCS)")
Energy Medicine
ACUP
forest(res_m %>% filter(Intervention %in% "ACUP") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (ACUP)")
Natural Product
Hormones
MELAT
forest(res_m %>% filter(Intervention %in% "MELAT") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (MELAT)")
SECRET
forest(res_m %>% filter(Intervention %in% "SECRET") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (SECRET)")
OXYT
forest(res_m %>% filter(Intervention %in% "SECRET") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (SECRET)")
Supplementation
HERB
forest(res_m %>% filter(Intervention %in% "HERB") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (HERB)")
L-CARNIT
forest(res_m %>% filter(Intervention %in% "L-CARNIT") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (L-CARNIT)")
L-CARNO
forest(res_m %>% filter(Intervention %in% "L-CARNO") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (L-CARNO)")
NAC
forest(res_m %>% filter(Intervention %in% "NAC") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (NAC)")
PROB
forest(res_m %>% filter(Intervention %in% "PROB") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (L-CARNIT)")
SULFO
forest(res_m %>% filter(Intervention %in% "SULFO") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (SULFO)")
PUFA
res_pufa = res_m %>% filter(Intervention %in% "PUFA")
forest(res_pufa %>% filter(!outcome_general %in% safety) %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (PUFA)")
forest(res_pufa %>% filter(outcome_general %in% safety) %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (PUFA)")
VIT-D
forest(res_m %>% filter(Intervention %in% "VIT-D") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (VIT-D)")
Diets
forest(res_m %>% filter(Intervention %in% "DIET") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (DIET)")
Whole Medical Systems
AAI
forest(res_m %>% filter(Intervention %in% "AAI") %>%
as.data.frame(),
layout = "RevMan5", subgroup = "Outcome",subgroup.name = "",
leftcols = c("paper", "amstar.x", "amstar_rank", "Age", "GRADE",
"n_studies", "rob", "I2", "effect.ci"),
leftlabs = c("paper", "AMSTAR\n(key items)", "AMSTAR\n(Rank)", "Age", "GRADE",
"n-studies", "Low RoB", "I²", "eSMD + 95% CI"),
smlab = "Overlapping (AAI)")
S13. Sensitivity RCTs
res_rct = readxl::read_excel(
paste0(chemin, "UR_RCT_analysis.xlsx")) %>%
filter(intervention_type == "Complementary")
res_full = res_m[, c("Factor", "IN_meta", "design_num", "measure", "value",
"p_value", "value_CI", "n_studies", "total_n", "GRADE")]
names(res_full) = paste0(names(res_full), "_full")
res_full$Factor = res_full$Factor_full
res_full$sig_full = as.numeric(res_full$p_value_full) < 0.05
res_rct = res_rct[, c("Factor", "measure", "value", "p_value",
"value_CI", "n_studies", "total_n", "GRADE")]
names(res_rct) = paste0(names(res_rct), "_rct")
res_rct$Factor = res_rct$Factor_rct
res_rct$sig_rct = as.numeric(res_rct$p_value_rct) < 0.05
res_compare = dplyr::left_join(res_full,res_rct) %>%
arrange(design_num_full) %>%
filter((((GRADE_full != GRADE_rct) |
(sig_full != sig_rct))) & total_n_rct > 1)## Joining, by = "Factor"S14. Vignette The download link does not work, I will create an URL